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1000 Titel
  • What difference does multiple imputation make in longitudinal modeling of EQ-5D-5L data? Empirical analyses of simulated and observed missing data patterns
1000 Autor/in
  1. Roesel, Inka |
  2. Serna-Higuita, Lina Maria |
  3. Al Sayah, Fatima |
  4. Buchholz, Maresa |
  5. Buchholz, Dipl.-Psych., Dr. Ines |
  6. Kohlmann, Thomas |
  7. Martus, Peter |
  8. Feng, You-Shan |
1000 Erscheinungsjahr 2021
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2021-11-19
1000 Erschienen in
1000 Quellenangabe
  • 31(5):1521-1532
1000 Copyrightjahr
  • 2021
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1007/s11136-021-03037-3 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9023409/ |
1000 Publikationsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • Purpose!#!Although multiple imputation is the state-of-the-art method for managing missing data, mixed models without multiple imputation may be equally valid for longitudinal data. Additionally, it is not clear whether missing values in multi-item instruments should be imputed at item or score-level. We therefore explored the differences in analyzing the scores of a health-related quality of life questionnaire (EQ-5D-5L) using four approaches in two empirical datasets.!##!Methods!#!We used simulated (GR dataset) and observed missingness patterns (ABCD dataset) in EQ-5D-5L scores to investigate the following approaches: approach-1) mixed models using respondents with complete cases, approach-2) mixed models using all available data, approach-3) mixed models after multiple imputation of the EQ-5D-5L scores, and approach-4) mixed models after multiple imputation of EQ-5D 5L items.!##!Results!#!Approach-1 yielded the highest estimates of all approaches (ABCD, GR), increasingly overestimating the EQ-5D-5L score with higher percentages of missing data (GR). Approach-4 produced the lowest scores at follow-up evaluations (ABCD, GR). Standard errors (0.006-0.008) and mean squared errors (0.032-0.035) increased with increasing percentages of simulated missing GR data. Approaches 2 and 3 showed similar results (both datasets).!##!Conclusion!#!Complete cases analyses overestimated the scores and mixed models after multiple imputation by items yielded the lowest scores. As there was no loss of accuracy, mixed models without multiple imputation, when baseline covariates are complete, might be the most parsimonious choice to deal with missing data. However, multiple imputation may be needed when baseline covariates are missing and/or more than two timepoints are considered.
1000 Sacherschließung
lokal Surveys and Questionnaires [MeSH]
lokal Missing at random
lokal Article
lokal Health-related quality of life
lokal Humans [MeSH]
lokal Imputation
lokal Quality of Life/psychology [MeSH]
lokal Missing data
lokal EQ-5D
lokal Research Design [MeSH]
lokal Psychometrics/methods [MeSH]
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0002-4373-5539|https://orcid.org/0000-0001-5182-8295|https://frl.publisso.de/adhoc/uri/QWwgU2F5YWgsIEZhdGltYQ==|https://orcid.org/0000-0003-2055-1937|https://orcid.org/0000-0001-9729-6992|https://orcid.org/0000-0002-5956-8309|https://orcid.org/0000-0002-5386-5732|https://orcid.org/0000-0003-1509-3409
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1000 Erstellt am 2023-04-27T14:22:58.596+0200
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